A Perturbative Approach to the Analysis of Many-Compartment Models Characterized by the Presence of Waning Immunity

TL;DR

This paper introduces a perturbative method to analyze multi-compartment epidemiological models with waning immunity, aiding in optimizing vaccination strategies and understanding disease dynamics.

Contribution

It develops a novel perturbative approach for analyzing many-compartment models with waning immunity, providing insights into equilibria and vaccination effects.

Findings

Established conditions for endemic and disease-free equilibria.

Compared vaccination schemes using $R_0$ and equilibrium analysis.

Fitted the model to pertussis data in Canada.

Abstract

The waning of immunity after recovery or vaccination is a major factor accounting for the severity and prolonged duration of an array of epidemics, ranging from COVID-19 to diphtheria and pertussis. To study the effectiveness of different immunity level-based vaccination schemes in mitigating the impact of waning immunity, we construct epidemiological models that mimic the latter's effect. The total susceptible population is divided into an arbitrarily large number of discrete compartments with varying levels of disease immunity. We then vaccinate various compartments within this framework, comparing the value of $R_0$ and the equilibria locations for our systems to determine an optimal immunization scheme under natural constraints. Relying on perturbative analysis, we establish a number of results concerning the location, existence, and uniqueness of the system's endemic equilibria, as well as results on disease-free equilibria. In addition, we numerically simulate the dynamics associated with our model in the case of pertussis in Canada, fitting our model to available time-series data. Our analytical results are applicable to a wide range of systems composed of arbitrarily many ODEs.